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NVIDIA Generative AI Multimodal Sample Questions (Q24-Q29):

NEW QUESTION # 24
You are using NeMo to fine-tune a pre-trained language model for a specific text generation task. You want to implement a custom data augmentation technique to improve the model's robustness. Which of the following approaches is most appropriate for integrating your custom augmentation within the NeMo framework?

A. Use a separate data processing pipeline outside of NeMo and save the augmented data to disk before training.
B. Create a custom *Dataset* class that inherits from 'nemo.core.Dataset' and implements your augmentation within the '_getitem
C. Monkey-patch the existing NeMo data loading functions to inject your augmentation logic.
D. Modify the core NeMo library files to directly incorporate your augmentation logic.
E. Augment the data directly within the training loop, applying transformations to each batch before feeding it to the model. method.

Answer: B

Explanation:
Creating a custom 'Dataset' class that inherits from 'nemo.core.Dataset' is the recommended and most maintainable way to integrate custom data augmentation in NeMo. This allows you to leverage NeMo's data loading and processing pipelines while seamlessly incorporating your specific augmentation logic within the '_getitem method. Modifying core NeMo files (A) is strongly discouraged. Using a separate pipeline (C) disconnects augmentation from the NeMo workflow. Monkey-patching (D) is brittle. Augmenting within the training loop (E) can be inefficient.

NEW QUESTION # 25
You are building a text-to-image application using CLIP. You notice that the generated images often lack specific details mentioned in the text prompt. Which of the following techniques would be most effective in improving the fidelity and detail of the generated images, given the limitations of CLIP's text encoder?

A. Reducing the number of training steps for the diffusion model to prevent overfitting to the training data and promote generalization.
B. Applying prompt engineering techniques such as adding descriptive adjectives and context to the text prompt and fine-tuning the prompt with iterative feedback.
C. Increasing the temperature parameter of the diffusion model used in conjunction with CLIP to introduce more randomness and potentially more detail.
D. Training a custom text encoder from scratch with a larger dataset specifically tailored to your application's domain.
E. Using a larger image decoder network with more parameters to add detail during the image generation process.

Answer: B

Explanation:
Prompt engineering is the most practical and effective method for improving the fidelity of text-to-image generation with CLIP, without requiring extensive retraining or architecture changes. By carefully crafting and refining the text prompt, you can guide the generation process to produce images that more accurately reflect the desired details. Training a custom text encoder (A) is resource-intensive. While a larger image decoder (B) might help, it doesn't address the core issue of accurately capturing the prompt's meaning. Increasing temperature (D) can add randomness but not necessarily detail. Reducing training steps (E) could worsen performance.

NEW QUESTION # 26
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?

A. Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.
B. Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
C. Resize all images to a uniform size.
D. Normalize all text data to lowercase and remove punctuation.
E. Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.

Answer: A,B,C,D

Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.

NEW QUESTION # 27
You are building a system that uses a Generative A1 model that combines images and natural language prompts to create photorealistic images. The training process is computationally intensive. Which NVIDIA technology is best suited to accelerate the training of this Generative A1 model, especially if it is distributed across multiple GPUs?

A. NVIDIA TensorRT
B. NVIDIA NCCL
C. NVIDIA NeMo
D. NVIDIA DALI
E. NVIDIA optiX

Answer: B

Explanation:
NVIDIA NCCL (NVIDIA Collective Communications Library) is designed for efficient multi-GPU and multi-node communication. It enables faster distributed training of deep learning models by optimizing data transfers and synchronization between GPUs, thereby accelerating the entire training process. While OptiX is for ray tracing, TensorRT is for inference optimization, NeMo is for conversational A1 model development, and DALI is for data loading and preprocessing, NCCL is specifically designed for accelerating distributed training.

NEW QUESTION # 28
You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?

A. KL Divergence
B. Mean Squared Error (MSE)
C. Triplet Loss
D. Cross-entropy loss
E. Contrastive Loss

Answer: C

Explanation:
Triplet Loss is specifically designed for ranking tasks. It takes three inputs: an anchor (text query), a positive example (relevant image), and a negative example (irrelevant image). The loss function aims to minimize the distance between the anchor and the positive example while maximizing the distance between the anchor and the negative example. Contrastive loss works with pairs, not relative rankings. Cross-entropy, MSE, and KL Divergence are not suitable for ranking problems.

NEW QUESTION # 29
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100% Valid NVIDIA NCA-GENM PDF Dumps and NCA-GENM Exam Questions

Exam Questions For NVIDIA NCA-GENM With 1 year Of Updates

NVIDIA Generative AI Multimodal Sample Questions (Q24-Q29):

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